class_docstr: Alternating Least Square to solve Canonical Polyadic Decompositon.
  
      
docstr: Hadamard productsof the gramian matrix without the `n`-th matrix.
  
  Args:
      A (list of numpy.ndarray): matrices where the number of columns for each matrix is the same.
      n (int): N-th matrix which is omitted when computing Gramian matrix.
docstr_tok: ["Hadamard","productsof","the","gramian","matrix","without","the","`","n","`","-th","matrix",".","Args",":","A","(","list","of","numpy.ndarray",")",":","matrices","where","the","number","of","columns","for","each","matrix","is","the","same",".","n","(","int",")",":","N-th","matrix","which","is","omitted","when","computing","Gramian","matrix","."]
code: def hadamard_products_of_gramians(self, A, n):
      """Hadamard productsof the gramian matrix without the `n`-th matrix.
  
      Args:
          A (list of numpy.ndarray): matrices where the number of columns for each matrix is the same.
          n (int): N-th matrix which is omitted when computing Gramian matrix.
  
      """
      order = list(range(n)) + list(range(n + 1, len(A)))
      V = 1.0
      for o in order:
          V = V * np.dot(A[o].T, A[o])
      return V
  
file_path: nnabla/python/src/nnabla/utils/factorization/cpd.py
imports: from functools import reduce
  import numpy
  from scipy.linalg import pinv
label_desc: Use image node_id 5 for calling the ALS obj's underlying member method code with example usage: obj.hadamard_products_of_gramians(A, n) and returns: V